AN INVESTIGATION OF DYNAMIC TOPIC MODELING FOR REAL-TIME AND EVOLVING TEXTUAL DATA USING DTM, BERTOPIC, RECURRENT NEURAL NETWORKS AND PROPOSED HYBRID DTM WITH RNN ALGORITHMS

Given the dynamic nature of textual data, Dynamic Topic Modeling has become an effective real-time analysis tool for streams of textual data. The objective of this study is to present a thorough review of different dynamic topic modeling strategies, such as advanced neural network-based methods like Recurrent Neural Networks (RNN), recent methodology like BERTopic, and traditional approaches like DTM. It also looks at the possible advantages and difficulties of combining RNN and DTM in a hybrid framework. We explore the effectiveness of these techniques in capturing temporal dynamics, identifying changing subjects, and offering insights into the underlying structures of the data through empirical evaluations on real-world textual datasets. Using the "Advanced Topic Modeling for Research Articles 2.0" dataset, this study assesses the methods according to a number of criteria, including accuracy, recall, precision, coherence, perplexity, and F-score. This research also assesses the subject modeling performance, scalability, and flexibility of our hybrid DTM and RNN strategy in relation to real-time and dynamic textual data, in comparison with other methods. The outcomes of our trials highlight the benefits of this hybrid strategy and offer insightful information to practitioners and researchers who want to use dynamic topic modeling for textual data analysis that is dynamic and real-time.